A framework for extended belief rule base reduction and training with the greedy strategy and parameter learning

The extended belief rule-based system has been used in the field of decision making in recent years for its advantage of expressing various kinds of information under uncertainty, where the extended belief rule base (EBRB) is used to store various types of uncertain knowledge in the form of belief structures. However, as data such as expert knowledge and experimental data is used to directly generate the EBRB, there could be noisy and redundant rules that not only increase the computation cost but also reduce the accuracy. To this end, a novel framework for EBR reduction and training with the greedy strategy and parameter learning is proposed in this paper. Firstly, a greedy-based EBRB reduction method is proposed, where noisy and redundant rules are be searched and removed. Then, the EBRB training method using parameter learning is introduced, where the parameters of the EBRB are trained to increase its accuracy. Next, the framework for EBRB reduction and training is introduced, and the procedure of the proposed method is detailed. Finally, two case studies are conducted to demonstrate the effectiveness and efficiency of the proposed method, and the results show that the proposed method could reduce the size of the EBRB while increasing its accuracy.